A Comparative Study of Political Discourse Features in English and Arabic
Bibliographic record
Abstract
This article is devoted to examine political discourse, in particular features of political speeches in English and Arabic Language. Political speeches are often shaped in a specific cultural and social context, using various linguistic features to persuade the public of the speaker’s goals. The study has two aims: firstly, it intends to highlight the prominent features of political discourse in English and Arabic. For example, the use of metaphor and metonymy, pronouns, intertextuality, repetition, style and code-switching. In addition, the study examines the way these features were employed by the speakers. Secondly, the comparison across English and Arabic language establishes similarities and differences between the features of political discourse in English and Arabic, and understands to what extent are the features of political discourse universal and shared between languages, and to further examine in which ways they differ. Three main features were identified as shared between the two languages: pronouns, repetition, and intertextuality. Even though there were shared features, it emerges from the study that these features, as well as others, are employed differently based on the language convention and the culture it exists in.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".